Fieldwork without the field: Navigating qualitative research in pandemic times
Bibliographic record
Abstract
More than ever before, the COVID-19 pandemic has required qualitative researchers to develop open-ended, flexible, and creative approaches to continuing their work. This reality includes the adoption of open-ended research goals, a willingness to continually adapt to unpredictable and changing (viral) circumstances, and a commitment to opening toward and adhering to participants' preferences. This ethos is entrenched in a web of moral responsibility and a future anteriorized ethics. We reflect on pandemic-era ethical and methodological considerations in light of Fortun's studies of toxic contamination, research conducted in conflict settings, and researcher experiences during the early stages of COVID-19. Drawing from our own experiences and bearing in mind our own entangled web(s) of moral responsibility, we explore the future anteriorized ethics and methodological landscape of the “new normal” pandemic (potentially endemic) era. We reflect on what data we are able to gather and what data we dare to gather in the context of COVID-19, ultimately asking how qualitative researchers can maintain a safe and ethical environment for conducting research. To this end, we emphasize a recognition of our obligations to our research partners and ourselves in order to reduce risk by turning doubts and concerns into opportunities during project development and fieldwork and transforming participants into collaborators in spaces of uncertainty. Through targeted reflections on our processes of adaptation in research, we examine how scholars can perform relatedness, knowledge, reasonableness, and care in the midst of a risky, compromised research context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.285 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.046 | 0.085 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".